code: def __init__(self):
      super(FeatureAugment, self).__init__()
  
      # pre-defined feature functions
      def degree_fun(graph, **kwargs):
          # TODO: if bin_edges is None, do regression
          return [d for _, d in graph.G.degree()]
  
      def centrality_fun(graph, **kwargs):
          # TODO multiple feature dimensions
          centrality = nx.betweenness_centrality(graph.G)
          return [centrality[x] for x in graph.G.nodes]
  
      def path_len_fun(graph, **kwargs):
          return [
              np.mean(
                  list(
                      nx.shortest_path_length(
                          graph.G, source=x
                      ).values()
                  )
              )
              for x in graph.G.nodes
          ]
  
      def edge_path_len_fun(graph, **kwargs):
          return [
              np.mean(
                  list(
                      nx.shortest_path_length(
                          graph.G, source=x
                      ).values()
                  )
              )
              for x in graph.G.nodes
          ]
  
      def pagerank_fun(graph, **kwargs):
          # TODO multiple feature dimensions
          pagerank = nx.pagerank(graph.G)
          return [pagerank[x] for x in graph.G.nodes]
  
      def identity_fun(graph, **kwargs):
          if "feature_dim" not in kwargs:
              raise ValueError("Argument feature_dim not supplied")
          return compute_identity(
              graph.edge_index, graph.num_nodes, kwargs["feature_dim"]
          )
  
      def clustering_coefficient_fun(graph, **kwargs):
          return list(nx.clustering(graph.G).values())
  
      def const_fun(graph, **kwargs):
          # directly return the torch tensor representation
          return torch.ones(graph.num_nodes)
  
      def onehot_fun(graph, **kwargs):
          # directly return the torch tensor representation
          # return torch.arange(graph.num_nodes)
          return torch.randperm(graph.num_nodes)
  
      def graph_laplacian_spectrum_fun(graph, **kwargs):
          # first eigenvalue is 0
          spectrum = nx.laplacian_spectrum(graph.G)[1:]
          feature_dim = kwargs["feature_dim"]
          if len(spectrum) > feature_dim:
              spectrum = spectrum[:feature_dim]
          return torch.tensor(spectrum)
  
      def graph_path_len_fun(graph, **kwargs):
          path = nx.average_shortest_path_length(graph.G)
          return torch.tensor([path])
  
      def graph_clustering_fun(graph, **kwargs):
          clustering = nx.average_clustering(graph.G)
          return torch.tensor([clustering])
  
      self.feature_dict = {
          "node_degree": degree_fun,
          "node_betweenness_centrality": centrality_fun,
          "node_path_len": path_len_fun,
          "node_pagerank": pagerank_fun,
          "node_clustering_coefficient": clustering_coefficient_fun,
          "node_identity": identity_fun,
          "node_const": const_fun,
          "node_onehot": onehot_fun,
          "edge_path_len": edge_path_len_fun,
          "graph_laplacian_spectrum": graph_laplacian_spectrum_fun,
          "graph_path_len": graph_path_len_fun,
          "graph_clustering_coefficient": graph_clustering_fun,
      }
  
      self.feature_dict = {
          **register.feature_augment_dict,
          **self.feature_dict,
      }
  
      for key, fun in self.feature_dict.items():
          self.feature_dict[key] = create_augment_fun(
              self.feature_dict[key], key
          )
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 1 to create a new FeatureAugment object from inherited base classes: nn with example: obj = FeatureAugment()